{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/carl-hui/software/env/AI/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold, RepeatedKFold\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "import time\n",
    "from pyspark.sql import SparkSession\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "import warnings\n",
    "from autogluon import TabularPrediction as task\n",
    "import pickle\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from skdist.distribute.search import DistGridSearchCV\n",
    "from sklearn.ensemble import BaggingClassifier,RandomForestClassifier,ExtraTreesClassifier,GradientBoostingClassifier\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_data = pd.read_csv('data_demo4.csv')\n",
    "# train_all_data=all_data[:28000]\n",
    "# test_all_data=all_data[28000:]\n",
    "# train_all_data[\"type\"] = train_all_data[\"type\"].map({'围网':0,'刺网':1,'拖网':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "NumExpr defaulting to 8 threads.\n"
     ]
    }
   ],
   "source": [
    "train_all_data = pd.read_hdf('data/train_transform.h5')\n",
    "test_all_data = pd.read_hdf('data/test_transform.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_all_data.drop(['type'], axis=1)\n",
    "y_train = train_all_data[\"type\"]\n",
    "X_test = test_all_data.drop(['ship'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = pd.read_csv('data/feature_importance.csv')\n",
    "feature_importance_list = feature_importance.loc[feature_importance[\"importance\"] != 0][\"feature_name\"].tolist()\n",
    "\n",
    "X_train=X_train[feature_importance_list]\n",
    "X_test=X_test[feature_importance_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#boruta 模型\n",
    "with open(\"data/feat_selector.pkl\", 'rb') as fo:     \n",
    "    feat_selector = pickle.load(fo)\n",
    "    \n",
    "X_train = feat_selector.transform(X_train.values)\n",
    "X_test = feat_selector.transform(X_test.values)\n",
    "\n",
    "X_train=pd.DataFrame(X_train)\n",
    "X_test=pd.DataFrame(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv(X_train, X_test, y_train, model, nfold, seed, num_classes=3,knn=False):\n",
    "    train_pred = np.zeros((len(X_train), num_classes))\n",
    "    test_pred = np.zeros((len(X_test), num_classes))\n",
    "    \n",
    "    kfolder = StratifiedKFold(n_splits=nfold, shuffle=True, random_state=seed)\n",
    "    for fold_id, (trn_idx, val_idx) in enumerate(kfolder.split(X_train,y_train)):\n",
    "        x_tr = X_train.iloc[trn_idx]; x_te = X_train.iloc[val_idx]\n",
    "        y_tr = y_train.iloc[trn_idx]; y_te = y_train.iloc[val_idx]\n",
    "        \n",
    "        print(f'\\nFold_{fold_id}  ==========================================\\n')\n",
    "        if not knn:\n",
    "            model.fit(x_tr, y_tr,eval_set=[(x_tr, y_tr), (x_te, y_te)])\n",
    "        else:\n",
    "            model.fit(x_tr, y_tr)\n",
    "            \n",
    "        val_pred = model.predict_proba(x_te)\n",
    "        train_pred[val_idx] += val_pred\n",
    "        val_pred = np.argmax(val_pred, axis=1)\n",
    "        print(fold_id, 'f1', metrics.f1_score(y_te, val_pred, average='macro'))\n",
    "        \n",
    "        test_pred += model.predict_proba(X_test)\n",
    "    print(fold_id, 'f1', metrics.f1_score(y_train, np.argmax(train_pred, axis=1), average='macro'))\n",
    "    return train_pred,test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "    voting_clf = VotingClassifier(estimators=[\n",
    "            (\"lgb_clf\",lgb.LGBMClassifier(learning_rate=0.28810319992794875,\n",
    "                                         min_child_samples=7, #它的值取决于训练数据的样本个树和num_leaves. 将其设置的较大可以避免生成一个过深的树, 但有可能导致欠拟合。\n",
    "                                         max_depth=7, #设置树深度，深度越大可能过拟合\n",
    "                                         lambda_l1=2,boosting=\"gbdt\",objective=\"multiclass\",\n",
    "                                         n_estimators=2000,metric='multi_error',num_class=3,\n",
    "                                         feature_fraction=0.75,bagging_fraction=0.85,seed=99,\n",
    "                                         num_threads=20,verbose=-1,n_jobs=-1,device=\"cpu\")),\n",
    "#             (\"gdbt_clf\",GradientBoostingClassifier(n_estimators=1200, max_depth=9, \n",
    "#                                                         min_samples_split=6, learning_rate=0.22461383601711735)),\n",
    "            (\"xgb_clf\",xgb.XGBClassifier(max_depth=3,learning_rate=0.14230129295508215,n_estimators=900,\n",
    "                                  silent=True,objective='multi:softmax')),\n",
    "            (\"cat_clf\",CatBoostClassifier(iterations=1600, depth=4,learning_rate=0.28758790269878165)),\n",
    "#             (\"adaboost_clf\",AdaBoostClassifier(DecisionTreeClassifier(max_depth=9, min_samples_split=20, min_samples_leaf=5),\n",
    "#                          algorithm=\"SAMME\",\n",
    "#                          n_estimators=1000, learning_rate=0.7))\n",
    "        ],voting=\"soft\")\n",
    "    voting_train_pred,voting_test_pred=cv(X_train, X_test, y_train, voting_clf, 20, 2020,knn=True)\n",
    "    return voting_train_pred,voting_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0 f1 0.9379359342662094\n",
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      "1 f1 0.897660659982838\n",
      "\n",
      "Fold_2  ==========================================\n",
      "\n"
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      "2 f1 0.8744795516136978\n",
      "\n",
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      "5 f1 0.9013564014096952\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1573:\tlearn: 0.0250205\ttotal: 10.7s\tremaining: 176ms\n",
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      "6 f1 0.902123579232013\n",
      "\n",
      "Fold_7  ==========================================\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-e3223a19651a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtrain_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-e3223a19651a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mThreadPoolExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtrain_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_tb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    624\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/concurrent/futures/thread.py\u001b[0m in \u001b[0;36mshutdown\u001b[0;34m(self, wait)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_threads\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m                 \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m     \u001b[0mshutdown\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_base\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1045\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1058\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1061\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=10) as t: \n",
    "    f1 = t.submit(train)\n",
    "    train_pred,test_pred = f1.result()\n",
    "\n",
    "end = time.time()\n",
    "print(str(end-start))  #841.635686"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-02751698088c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'train_pred' is not defined"
     ]
    }
   ],
   "source": [
    "plabels = np.argmax(train_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('oof f1', metrics.f1_score(y_train,plabels, average='macro'))\n",
    "print('train accuracy', accuracy_score(y_train,plabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生产"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = test_all_data[['ship']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "production_pred = np.argmax(test_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['pred'] = production_pred\n",
    "\n",
    "print(sub['pred'].value_counts(1))\n",
    "sub['pred'] = sub['pred'].map({0:'围网',1:'刺网',2:'拖网'})\n",
    "sub.to_csv('data/result.csv', index=None, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# voting_clf lgb xgb cat 3折\n",
    "# oof f1 0.8720838571825237\n",
    "# train accuracy 0.9008571428571429\n",
    "\n",
    "# lgb\n",
    "# oof f1 0.8590813093597846\n",
    "# train accuracy 0.8907142857142857\n",
    "\n",
    "# xgb\n",
    "# oof f1 0.8696092826220211\n",
    "# train accuracy 0.8978571428571429\n",
    "\n",
    "# cat\n",
    "# oof f1 0.858749246566959\n",
    "# train accuracy 0.8918571428571429\n",
    "\n",
    "# auto stacking\n",
    "# oof f1 0.8695501080812628\n",
    "# train accuracy 0.8971428571428571\n",
    "\n",
    "# xgb stacking\n",
    "# oof f1 0.850256901032857\n",
    "# train accuracy 0.8828571428571429\n",
    "\n",
    "# voting_clf lgb xgb cat 5 折\n",
    "# oof f1 0.879962561909847\n",
    "# train accuracy 0.9062857142857143\n",
    "\n",
    "# voting_clf lgb xgb cat 10 折\n",
    "# oof f1 0.8894536793850293  线上0.86563  = 0.02382\n",
    "# train accuracy 0.9134285714285715"
   ]
  }
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